Sometimes working on advanced technologies comes with the peril of NDAs … which limit what I can talk about… but it is nice to see yet another of our projects feature in Keynote speech by Satya Nadella, this time at Microsoft //BUILD 2018. Proud to be the lead architect working on advanced Machine Learning solutions and pipelines at Jabil.

“Azure SQL Data Sync allows users to synchronize data between Azure SQL Databases and SQL Server databases in one-direction or bi-direction. This feature was first introduced in 2012. By that time, people didn’t host a lot of large databases in Azure. Some size limitations were applied when we built the data sync service, including up to 30 databases (five on-premises SQL Server databases) in a single sync group, and up to 500 tables in any database in a sync group.

Today, there are more than two million Azure SQL Databases and the maximum database size is 4TB. But those limitations of data sync are still there. It is mainly because that syncing data is a size of data operation. Without an architectural change, we can’t ensure the service can sustain the heavy load when syncing in a large scale. We are working on some improvements in this area. Some of these limitations will be raised or removed in the future. In this article, we are going to show you how to use data sync to sync data between large number of databases and tables, including some best practices and how to temporarily work around database and table limitations”…

Azure Event Grid generally available
Easily subscribe and react to interesting events happening in any Azure service or in your own apps with Azure Event Grid, a fully managed event routing service. Event Grid removes the need to continuously poll by reliably routing events at high scale and low latency. In addition, take advantage of the rich coverage and reactive event model enabled by Event Grid in your serverless architectures. These features were added as part of the general availability (GA) release:

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Richer scenarios enabled through integration with more services: Azure Storage General Purpose v2 accounts were added; Azure IoT Hub and Azure Service Bus were added as new event publishers; and Azure Event Hubs was added as a new destination for events.

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Availability in more regions: It’s now available in the following regions: West US, East US, West US 2, East US 2, West Central US, Central US, West Europe, North Europe, Southeast Asia, and East Asia, with more coming soon.

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Increased reliability and service-level agreement (SLA): We now have a 24-hour retry policy with exponential back off for event delivery. We also offer 99.99 percent availability with a financially backed SLA for your production workloads.

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Better developer productivity: New management and data plane SDKs help make the development process smoother.

Event Grid is now generally available. GA pricing will be effective for billing periods beginning on or after April 1, 2018. Learn more on the overview and pricing webpages or via the Azure Blog.

The guide is structured around a basic pivot: The distinction between relational data and non-relational data.

Relational data is generally stored in a traditional RDBMS or a data warehouse. It has a pre-defined schema (“schema on write”) with a set of constraints to maintain referential integrity. Most relational databases use Structured Query Language (SQL) for querying. Solutions that use relational databases include online transaction processing (OLTP) and online analytical processing (OLAP).

Non-relational data is any data that does not use the relational model found in traditional RDBMS systems. This may include key-value data, JSON data, graph data, time series data, and other data types. The term NoSQL refers to databases that are designed to hold various types of non-relational data. However, the term is not entirely accurate, because many non-relational data stores support SQL compatible queries. Non-relational data and NoSQL databases often come up in discussions of big data solutions. A big data architecture is designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems.

Within each of these two main categories, the Data Architecture Guide contains the following sections:

Concepts. Overview articles that introduce the main concepts you need to understand when working with this type of data.

Scenarios. A representative set of data scenarios, including a discussion of the relevant Azure services and the appropriate architecture for the scenario.

Technology choices. Detailed comparisons of various data technologies available on Azure, including open source options. Within each category, we describe the key selection criteria and a capability matrix, to help you choose the right technology for your scenario.

This guide is not intended to teach you data science or database theory — you can find entire books on those subjects. Instead, the goal is to help you select the right data architecture or data pipeline for your scenario, and then select the Azure services and technologies that best fit your requirements. If you already have an architecture in mind, you can skip directly to the technology choices.

Azure offers a rich data and analytics platform for customers and ISVs seeking to build scalable BI and reporting solutions. However, customers face pragmatic challenges in building the right infrastructure for enterprise-grade production systems. They have to evaluate the various products for security, scale, performance and geo-availability requirements. They have to understand service features and their interoperability, and they must plan to address any perceived gaps using custom software. This takes time, effort, and many times, the end-to-end system architecture they design is sub-optimal.